Suboptimal Patient Research Articles

THE ASSESSMENT OF KNOWLEDGE AND PRACTICES OF NURSES REGARDING WOUND CARE

Proper wound care is critical for patient recovery and infection prevention. Inadequate knowledge and inconsistent practices among nurses can lead to suboptimal patient outcomes, particularly in low- and middle-income countries like Pakistan. Objective: To assess the knowledge and practices of nurses regarding wound care in a tertiary care hospital in Lahore, Pakistan. Methods: A descriptive cross-sectional study was conducted among 133 nurses working in medical wards, surgical wards, and ICUs. Data were collected using a structured questionnaire assessing demographic characteristics, knowledge, and practices related to wound care. Descriptive and inferential statistics were analyzed using SPSS version 26. Results: Among the participants, 73.7% demonstrated good knowledge of wound care, while 26.3% had poor knowledge. However, only 54.1% exhibited good practices, with 45.9% demonstrating suboptimal adherence to wound care protocols. Factors such as insufficient training and high workloads were identified as potential barriers to effective practice. Conclusion: Although nurses possessed good theoretical knowledge of wound care, significant gaps in practice were observed. Targeted training, resource provision, and strict adherence to evidence-based guidelines are essential for improving wound care practices and reducing the incidence of SSIs in Pakistan.

Hospital staff's experiences of unsafe staffing levels and employers' responses to their concerns: a survey.

There is a wealth of evidence that demonstrates a link between inadequate nurse staffing levels and suboptimal patient care. Over the last decade, policies and legislation on safe nurse staffing levels have been developed for specific settings and countries in the UK, all of which emphasise employers' responsibility in responding to staff's concerns about unsafe staffing. However, the effectiveness of mechanisms for monitoring and responding to such concerns is unclear. This article provides an overview of some of the results of a union-led pilot survey of acute hospital staff that aimed to explore the prevalence of unsafe staffing levels and employers' responses to concerns raised by staff about staffing levels. Overall, the results demonstrate that hospital staff working in clinical roles often regarded staffing levels as unsafe and that the delivery of nursing interventions was often disrupted on shifts reported as unsafe. In addition, most staff who had raised concerns about staffing levels perceived their employer's response as unsatisfactory. Employers appeared to rely on staff redeployment or temporary staff to address staffing shortfalls, but these are not always effective or viable options. Employers need to explore alternative means of alleviating staffing shortfalls and of supporting their staff.

Decision-making and ethical dilemmas experienced by hospital physicians during the COVID-19 pandemic in the Czech Republic

BackgroundDuring the COVID-19 pandemic, global healthcare systems faced unprecedented challenges, with a lack of resources and suboptimal patient care emerging as primary concerns.MethodsOur research, using a comprehensive 24-item electronic questionnaire, “Reflections on the Provision of Healthcare during the COVID-19 Pandemic,” delved into the experiences of 938 physicians across the Czech Republic.ResultsOver fifty per cent observed a “lower standard of care” compared to pre-pandemic levels. A division arose among physicians regarding a decision’s medical, ethical, or legal basis, with a notable gender disparity: male doctors leaned towards medical perspectives, whereas females accented the ethical perspective. Decision-making concerning health care limitations required agreement among the physicians on duty, interdisciplinary teams, or shift supervisors. Physicians reported varying degrees of patient or family participation in health care decisions. Variables such as age, pre-existing health conditions, and life expectancy influenced care decisions. Surprisingly, half of the physicians faced refusals of patients’ transportation to better-equipped facilities due to resource constraints. One-third of physicians never discuss the decision about care limitation and other options with patients or their families. As a result, almost fifty per cent of the physicians rarely or never imparted information about care limitations to patients.ConclusionThe survey shed light on the profound ethical dilemmas hospital physicians face across different types of healthcare facilities during the pandemic. It uncovered the need for open dialogue and scholarly debate on resource allocation and strengthening the role of patients and their families in care decisions in future healthcare crises.

A new perspective on targeting pulmonary arterial hypertension: Programmed cell death pathways (Autophagy, Pyroptosis, Ferroptosis).

Pulmonary arterial hypertension (PAH) is a severe cardiovascular disease characterized by elevated pulmonary vascular resistance, progressive increases in pulmonary artery pressures, ultimately leading to right-sided heart failure, and potentially mortality. Pulmonary vascular remodeling is pivotal in PAH onset and progression. While targeted drug therapies have notably ameliorated PAH prognosis, current medications primarily focus on vascular vasodilation, with limited ability to reverse pulmonary vascular remodeling fundamentally, resulting in suboptimal patient prognoses. Cellular death in pulmonary vasculature, once thought to be confined to apoptosis and necrosis, has evolved with the identification of pyroptosis, autophagy, and ferroptosis, revealing their association with vascular injury in PAH. These novel forms of regulated cellular death impact reactive oxygen species (ROS) generation, calcium stress, and inflammatory cascades, leading to pulmonary vascular cell loss, exacerbating vascular injury, and mediating adverse remodeling, inflammation, immune anomalies, and current emerging mechanisms (such as endothelial-mesenchymal transition, abnormal energy metabolism, and epigenetic regulation) in the pathogenesis of PAH. This review comprehensively delineates the roles of autophagy, pyroptosis, and ferroptosis in PAH, elucidating recent advances in their involvement and regulation of vascular injury. It juxtaposes their distinct functions in PAH and discusses the interplay of these programmed cell deaths in pulmonary vascular injury, highlighting the benefits of combined targeted therapies in mitigating pulmonary arterial hypertension-induced vascular injury, providing novel insights into targeted treatments for pulmonary arterial hypertension.

Ethical considerations on the use of big Data and Artificial Intelligence in kidney research from the ERA ethics committee.

In the current paper, we will focus on requirements to ensure big data can advance the outcomes of our patients suffering from kidney disease. The associated ethical question is whether and how we as a nephrology community can and should encourage the collection of big data of our patients. We identify some ethical reflections on the use of big data, and their importance and relevance. Furthermore, we balance advantages and pitfalls and discuss requirements to make legitimate and ethical use of big data possible. The collection, organization and curation of data come upfront in the pipeline prior to any analyses. Great care must therefore be taken to ensure quality of the data at this stage, to avoid the garbage in garbage out problem and suboptimal patient care as a consequence of such analyses. Access to the data should be organized so that correct and efficient use of data is possible. This means that data must be stored safely, so that only those entitled to do so can access them. At the same time, those who are entitled to access the data should be able to do so in an efficient way, so as not to hinder relevant research. Analysis of observational data is itself prone to many errors and biases. Each of these biases can finally result in provision of low-quality medical care. Secure platforms should therefore also ensure correct methodology is used to interpret the available data. This requires close collaboration of a skilled workforce of experts in medical research and data scientists. Only then will our patients be able to benefit fully from the potential of AI and big data.

Machine Learning Approaches to Predict No-Shows in Saudi Arabian Primary and General Healthcare Settings

Introduction Frequent no-shows in health care appointments pose significant challenges, leading to wasted resources and suboptimal patient outcomes. Traditional mitigation methods, such as reminder messages and phone calls, often fall short, particularly in regions with less robust healthcare infrastructures. This study leverages machine learning techniques to develop a predictive model that identifies patients at high risk of missing appointments using data from the Mawid scheduling system in Southern Saudi Arabia. Methods A retrospective cross-sectional design was employed, focusing on visits to primary healthcare centers [PHCCs] and general hospitals. Data spanning 10 months from June 2023 to March 2024 were collected from Mawid, encompassing over one million observations and 18 features, including appointment details, patient demographics, and weather conditions. Machine learning models, such as Decision Trees, Random Forests, Naive Bayes, Logistic Regression, and Artificial Neural Networks [ANN], have been developed and evaluated based on accuracy, precision, recall, F1 Score, and Area under the curve (AUC). Results The findings revealed that 55.07% of appointments were not attended. The Random Forest model exhibited superior performance with an accuracy of 0.765 and an AUC of 0.852, particularly when weather-related features were included. The ANN models also performed robustly with an AUC value of approximately 0.836. This study identified significant regional, seasonal, and environmental factors affecting no-show rates, with higher no-show rates occurring during certain months and under specific weather conditions. Regular appointments and PHCCs showed different attendance patterns than hospitals, while walk-in appointments did. Conclusion Machine learning models, particularly Random Forests and ANNs, can effectively predict healthcare appointment no-shows, thereby allowing for better resource allocation and patient care. Recognizing the influence of regional and environmental factors is crucial for developing targeted interventions to reduce the no-show rates. Future research should explore integrating more contextual data to further refine these predictive models and to enhance healthcare delivery and operational efficiency.

Mitigating Cognitive Biases in Clinical Decision-Making Through Multi-Agent Conversations Using Large Language Models: Simulation Study.

Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes. Addressing these biases presents a formidable challenge in the medical field. This study aimed to explore the role of large language models (LLMs) in mitigating these biases through the use of the multi-agent framework. We simulate the clinical decision-making processes through multi-agent conversation and evaluate its efficacy in improving diagnostic accuracy compared with humans. A total of 16 published and unpublished case reports where cognitive biases have resulted in misdiagnoses were identified from the literature. In the multi-agent framework, we leveraged GPT-4 (OpenAI) to facilitate interactions among different simulated agents to replicate clinical team dynamics. Each agent was assigned a distinct role: (1) making the final diagnosis after considering the discussions, (2) acting as a devil's advocate to correct confirmation and anchoring biases, (3) serving as a field expert in the required medical subspecialty, (4) facilitating discussions to mitigate premature closure bias, and (5) recording and summarizing findings. We tested varying combinations of these agents within the framework to determine which configuration yielded the highest rate of correct final diagnoses. Each scenario was repeated 5 times for consistency. The accuracy of the initial diagnoses and the final differential diagnoses were evaluated, and comparisons with human-generated answers were made using the Fisher exact test. A total of 240 responses were evaluated (3 different multi-agent frameworks). The initial diagnosis had an accuracy of 0% (0/80). However, following multi-agent discussions, the accuracy for the top 2 differential diagnoses increased to 76% (61/80) for the best-performing multi-agent framework (Framework 4-C). This was significantly higher compared with the accuracy achieved by human evaluators (odds ratio 3.49; P=.002). The multi-agent framework demonstrated an ability to re-evaluate and correct misconceptions, even in scenarios with misleading initial investigations. In addition, the LLM-driven, multi-agent conversation framework shows promise in enhancing diagnostic accuracy in diagnostically challenging medical scenarios.

Big Data in Pharmacy: Transforming Patient Care with Analytics

The integration of big data analytics in pharmacy is revolutionizing patient care by providing insights that enhance medication management and therapeutic outcomes. By leveraging vast amounts of health data, pharmacists can deliver more personalized treatment and optimize drug therapy. Despite the potential benefits, existing methods in pharmacy often suffer from data silos, lack of interoperability, and insufficient analytical capabilities, leading to suboptimal patient outcomes and inefficient medication management. These limitations hinder the effective utilization of data for real-time decision-making in patient care. To address these challenges, it propose the Patient Care Transformation using Big Data Analytics (PCT-BDA) framework, which facilitates a holistic approach to patient data integration and analysis within smart grid systems. This framework enables seamless data flow between various healthcare entities, improving collaboration and fostering a comprehensive view of patient health profiles. The proposed method emphasizes real-time analytics, predictive modeling, and machine learning algorithms to enhance decision-making processes in medication management. By utilizing big data analytics, pharmacists can better predict patient responses, identify potential drug interactions, and tailor therapy plans to individual needs. Preliminary findings from the implementation of the PCT-BDA framework indicate significant improvements in patient outcomes, including reduced medication errors, enhanced adherence to treatment protocols, and overall increased satisfaction with pharmacy services. This innovative approach highlights the transformative potential of big data analytics in optimizing patient care in the pharmacy sector.

Evaluating the Accuracy of Artificial Intelligence(AI)-Generated Synopses for Plasma Cell Disorder Treatment Regimens

BACKGROUND Concise, accurate, and real time synopses of the evidence base are critical to support treatment decision-making in hematology, especially in a very rapidly evolving space such as the treatment of plasma cell disorders (PCDs). Synopses are used in clinical practice guidelines, educational settings, and general clinical practice. This process of knowledge curation is time consuming and cumbersome, even when performed by a clinical expert. Artificial intelligence (AI) - specifically, Large Language Models (LLMs) have promise in this context; however, they are prone to hallucination and may provide inaccurate and out of date information. Morever, the degree to which individual LLMs perform in summarizing the clinical evidence base may vary widely. We objectively assessed the abilities of four LLMs to generate accurate, coherent, and relevant synopses for six widely used PCD regimens. METHODS We compared the performance of four popular LLMs: 1) Claude 3.5 (“Claude”); 2) ChatGPT 4.0 (“ChatGPT”); 3) Gemini; and 4) Llama-3.1 (“Llama”). Each LLM was prompted exactly as follows: “write a synopsis for the development and evolution of therapy with [regimen name] for PCD, using citations from the literature”, where [regimen name] was replaced with: 1) “Dara-RVd”; 2) “KRd”; 3) “VDT-PACE”; 4) “Dara-CyBorD”; 5) “Elranatamab”; and 6) “Talquetamab.” The generated synopses were assessed by two PCD physician specialists using Likert scales on six criteria: accuracy, completeness, relevance, clarity, hallucinations, and coherence; lower scores correspond to poor performance and higher scores to excellent performance. The reviewers were blinded to LLMs to minimize bias and conducted their evaluations independently. The evaluation process was recorded using REDCap. Mean scores with 95% confidence intervals (CI) were calculated across all domains. RESULTS There were marked differences in LLM performance across the six criteria. Claude demonstrated the highest performance in all domains, notably outperforming the other LLMs in accuracy: mean Likert score 3.92 (95% CI 3.54-4.29); ChatGPT 3.25 (2.76-3.74); Gemini 3.17 (2.54-3.80); Llama 1.92 (1.41-2.43); completeness: mean Likert score 4.00 (3.66-4.34); GPT 2.58 (2.02-3.15); Gemini 2.58 (2.02-3.15); Llama 1.67 (1.39-1.95); and lack of hallucinations: mean Likert score 4.00 (4.00-4.00); ChatGPT 2.75 (2.06-3.44); Gemini 3.25 (2.65-3.85); Llama 1.92 (1.26-2.57). Llama performed considerably poorer across all the studied domains, frequently providing inaccurate information and misinterpreted results. ChatGPT and Gemini had intermediate performance. Notably, none of the LLMs including Claude registered perfect accuracy, completeness, or relevance. CONCLUSION To our knowledge, this is the first rigorous evaluation of widely available LLMs for the task of evidence summarization in the PCD domain. In our evaluation of four LLMs for six PCD regimens, meaningful differences were evident across the LLMs. While Claude performed at a notably higher level, evidence synopses need to be as close to perfectly accurate and comprehensive to be usable for real-world clinical decision support. By this standard, even the best performing LLMs continue to require careful editing from a domain expert to become usable. Inaccurate and incoherent synopses could lead to suboptimal patient care if taken literally. We plan to repeat this experiment with newer generations of LLMs to evaluate their potential improvement over time.

Suboptimal patient positioning with panoramic and CBCT imaging: effects on dose

Suboptimal patient positioning with panoramic and CBCT imaging: effects on dose

Evaluating the feasibility of upper arm ECG for cardiac monitoring

Abstract Background Ambulatory electrocardiogram (ECG) monitoring is essential for detecting paroxysmal cardiac events and tracking long-term physiological parameters. However, traditional Holter devices are cumbersome, restrict movement, and can cause skin irritation, resulting in sub-optimal patient compliance during extended monitoring periods. Wearable devices utilizing non-conventional form factors are emerging as promising non-intrusive alternatives for ECG monitoring. Purpose To determine the optimal location for single-lead ECG acquisition on the upper arm and assess the feasibility of using dry electrode armbands for cardiac monitoring when coupled with robust ECG processing software. Methods Stationary ECGs from 20 healthy adult participants were recorded across a two-phase data collection protocol and processed with clinically validated ECG software. In the first phase, the average signal amplitudes of wet-electrode ECGs obtained from 30 different configurations across the mid-brachial and proximal-brachial regions of the upper arm were assessed. In the second, the performance of two proprietary dry-electrode armbands, featuring either a single-point or three-point electrode strap, were evaluated on the optimal upper arm configuration using QRS detection and heart rate (HR) metrics. Comparative analysis was performed throughout using data simultaneously acquired on a gold standard Holter device in modified lead III (MLIII) configuration. Performance metrics were calculated relative to manually generated signal annotations. Results Wet-electrode-acquired ECGs from the proximal-brachial region exhibited significantly higher average signal amplitudes (0.12 ± 0.06 mV) compared to those from the mid-brachial region (0.05 ± 0.04 mV) of the upper arm (p < 0.05). The optimal configuration (A-F), positioned at the lateral and medial extents of the proximal-brachial region, demonstrated comparable average signal amplitudes (0.16 ± 0.07 mV, 0.16 ± 0.06 mV, 0.19 ± 0.08 mV) for single-point dry, three-point dry, and wet-electrode setups, respectively. Upon processing with ECG software, data from optimal dry-electrode configurations showed excellent average QRS sensitivity and specificity of 98.2% and 98.9% for the one-point strap, and 98.9% and 99.8% for the three-point strap, respectively. HR metrics from the optimized dry-electrode setups were comparable in accuracy to those derived from the reference Holter device, with the average HR error being similar across the three-point dry electrode strap (0.57% ± 0.28%), one-point dry electrode strap (1.16% ± 1.46%), and Holter device (0.74% ± 1.50%). Conclusion(s) Optimized upper arm single-lead ECG with dry electrodes can yield signals with sufficient amplitude and quality for automated ECG analysis. Coupled with robust ECG signal processing software, the cardiac metrics obtained from the optimized upper arm setups exhibited comparable performance to those derived from medical Holter data.

Addressing integration in the organization of palliative care in Belgium: a multilevel ecosystems approach using the analytic hierarchy process (AHP) method

BackgroundPalliative care is becoming an essential component of healthcare, but there is insufficient research on how integration across different levels of care (micro, meso, and macro) is realized in practice. Without such integration, care may become fragmented, leading to suboptimal patient outcomes. While many studies have explored palliative care models, there is a gap in understanding how priorities for integrated care align across these levels within healthcare ecosystems. Specifically, it is unclear whether key actions at each level are shared, coordinated, and supported effectively, making it difficult to implement sustainable, cohesive care strategies. Our study aims to explore the extent to which important goals (i.e., priorities) are shared across the micro, meso, and macro levels of the palliative care ecosystem in Flanders, Belgium.MethodsWe applied a multimethod study using the analytic hierarchy process method (AHP). This consists of three sequential steps: a broad literature search and interviews with Belgian stakeholders (n = 12) to determine the criteria for the organization of integrated care; focus groups (n = 8) with patients, their relatives and caregivers to establish the completeness and relevance of the criteria; and prioritization of the criteria using a questionnaire among 305 Flemish participants (patients, relatives, caregivers and policy makers).ResultsOur findings revealed that integration is imbalanced, with priorities being most emphasized at the micro level (57%), followed by the meso (29%) and macro (14%) level. Functional enablers dominate at the macro (80%) and meso organizational level (67%), while normative enablers are emphasized at the meso professional (67%) and micro level (75%). Effective palliative care requires vertical coordination of these enablers: for instance, transparent communication with patients at the micro level depends on cross-organizational information exchange at the meso level, supported by a unified data system at the macro level.ConclusionAchieving integrated palliative care requires deliberate alignment of priorities across all levels of the ecosystem. While each level plays a unique role, palliative care is comprehensive and effective only by sharing both functional and normative enablers across micro, meso, and macro level.

Advancements in automated external and wearable cardiac defibrillators.

Survival statistics for out-of-hospital cardiac arrests remain unsatisfactory. Prompt defibrillation of shockable rhythms, such as ventricular fibrillation and pulseless ventricular tachycardia, is crucial for improving survival. Automated external defibrillators (AEDs) and wearable cardiac defibrillators (WCDs) seek to improve the survival rates following out-of-hospital cardiac arrests. We aim to review the indications, utility, advancements, and limitations of AEDs and WCDs, as well as their role in contemporary and future clinical practice. Recent advancements in these technologies, such as smartphone applications and drone delivery of AEDs and less inappropriate shocks and decreased size of WCDs, have increased their ubiquity and efficacy. However, implementation of this technology remains limited due to lack of resources and suboptimal patient adherence. Out of hospital cardiac arrests continue to pose a significant public health challenge. Advancements in AEDs and WCDs aim to facilitate prompt defibrillation of shockable rhythms with the goal of improving survival rates. However, they remain underutilized due to limited resources and suboptimal patient adherence. As these technologies continue to evolve to become smaller, lighter and more affordable, their utilization and accessibility are expected to improve.

Understanding Patient Flow from the Perspectives of Patient Movement Experts

Background: Poor patient flow or patient movement through a healthcare organisation can lead to adverse outcomes for patients and organisational inefficiency. Many hospitals have addressed suboptimal patient flow by increasing resources, such as bed stock and staffing; however, this is an unsustainable approach. In determining the nature of poor patient flow issues, it is important to collect data from healthcare professionals who manage patient flow daily. Doing so provides insights into the current state of patient flow management in its entirety, whilst also helping with the development of sustainable solutions. Methods: Thirteen semi-structured interviews were conducted with healthcare professionals who were directly involved with patient flow at a referring hospital in Tasmania, Australia. Results: Using a thematic analysis method, four major themes were developed. The first major theme was: ‘managing patient flow’ which centered around known and unknown demands on bed availability. The second theme, ‘communication for decisions’, highlighted the essential role of communication in maintaining patient flow. The third theme, ‘tools as enablers and barriers, relates to the number of software programs which can both help and hinder patient flow. The final theme of ‘increasing complexity’ is related to an apparent trend towards greater numbers of patients requiring care of an increasingly specialised nature. Conclusions: The findings of this study provide great insights into patient flow issues, with potential solutions identified to address them.

Advances in Gouty Arthritis Management: Integration of Established Therapies, Emerging Treatments, and Lifestyle Interventions.

Gouty arthritis, a prevalent inflammatory condition characterized by the deposition of monosodium urate crystals within joints, often results in debilitating pain and inflammation. Conventional therapeutic approaches, including nonsteroidal anti-inflammatory drugs, corticosteroids, and urate-lowering agents such as allopurinol and febuxostat, often have limitations such as adverse effects, drug interactions, and suboptimal patient compliance. This review presents a comprehensive overview of both established and emerging therapeutic strategies, developed between 2019 and 2024, for gouty arthritis; the review focuses on their mechanisms of action, efficacy, and safety profiles. Novel therapeutic approaches include pharmaceutical plant additives (e.g., Citrullus colocynthis, Atractylodes lancea), anti-inflammatory agents such as canakinumab and ozone therapy, and complementary therapies such as warm ginger compresses, Qingpeng ointment, and various lifestyle modifications. These strategies offer promising alternatives to conventional treatments by targeting uric acid metabolism, inflammatory pathways, and crystal formation, potentially reducing reliance on standard medications and minimizing adverse effects. Although therapies such as canakinumab have demonstrated significant efficacy in reducing gout flares, others such as polyphenol-rich foods offer favorable safety profiles. Further research, including large-scale clinical trials, is warranted to validate these findings and integrate these strategies into clinical practice to improve patient outcomes and quality of life.

Early Addition of Selexipag to Double Therapy for Pulmonary Arterial Hypertension

A subgroup analysis of a randomized clinical trial established the efficacy of selexipag plus background therapy (monotherapy or double oral therapy [DOT]) vs placebo plus background therapy and found that the addition of selexipag within 6 months had an added benefit. However, the timing of selexipag addition to DOT and the incremental benefit in clinical practice is not well studied. To compare triple oral therapy (TOT) consisting of selexipag, endothelin receptor antagonist (ERA), and phosphodiesterase type 5 inhibitor (PDE5i) vs DOT consisting of ERA and PDE5i. This comparative effectiveness study was conducted using data from the US Komodo claims database to emulate a randomized trial. Patients aged 18 years or older with pulmonary arterial hypertension (PAH) treated with ERA plus PDE5i with records from July 2015 through June 2022 were duplicated to TOT and DOT and artificially censored when observed treatment deviated from assigned treatment. Hypothetical randomization was emulated using inverse probability of treatment weighting, and the study accounted for censoring-induced selection bias using inverse probability of censoring weighting. A pooled logistic model estimated the per-protocol difference between treatment groups. Data were analyzed from November 2022 through July 2023. TOT (addition of selexipag within 3, 6, and 12 months of initiating DOT) vs DOT. Adjusted risk of all-cause hospitalization, PAH-related hospitalization, and PAH-related disease progression over a 2-year follow-up. A total of 2966 patients with PAH (mean [SD] age, 54.3 [14.0] years; 2125 female [71.6%]) met eligibility criteria. Adding selexipag within 6 months of ongoing DOT was associated with a reduction in risk for all-cause hospitalization (adjusted hazard ratio [aHR], 0.82; 95% CI, 0.72-0.94), PAH-related hospitalization (aHR, 0.81; 95% CI, 0.70-0.95), and PAH-related progression (aHR, 0.82; 95% CI, 0.70-0.95) vs DOT alone. There were no associations if selexipag was initiated within 12 months for all-cause hospitalization, PAH-related hospitalization, or PAH-related disease progression. The association remained with a greater decrease in risk for disease progression vs DOT for selexipag initiation within 3 months (aHR, 0.74; 95% CI, 0.61-0.90). This study found that early selexipag addition to ERA plus PDE5i was associated with a reduction in risk of hospitalization and disease progression. These findings suggest that delays in selexipag initiation likely contribute to suboptimal patient and health system outcomes.

When, How, and Barriers of Palliative Care in Intensive Care Unit: A Scoping Review

Palliative and near-death conditions in critical ill patients may occur, either predictable prognosis or acute conditions. Nurses are often unprepared for these care needs, resulting in suboptimal patient care. This study aims to review empirical evidence over the past 10 years regarding when to start, how to apply, and constraints during applying palliative care and end of life (EoL) care in intensive care. This review was conducted using journal database CINAHL, PubMed, Scopus, and WOS and selected article by PRISMA. The findings show EoL care begins when the medical team has assessed and approved by the patient and family including using the referral checklist format from intensive care to EoL care including the resources needed. In addition, during the upbringing process counselling and consultation with the family for any decision-making medical measures, nursing rounds, emotional and spiritual support and touches and hugs for the patient and family, including support for the entire medical team. However, there are also obstacles in the right communication time to families, discussions about death, and improving the competence of nurses through EoL education and training.

Abstract P333: Predicted Nationwide Health and Economic Impacts of the AMA MAP TM Hypertension Program to Improve Hypertension Control

Introduction: Lack of actionable blood pressure (BP) measurements, therapeutic inertia, and suboptimal patient self-management are three key barriers to better BP control. To help address these barriers, the American Medical Association developed the Measure Accurately, Act Rapidly, and Partner with Patients Hypertension (MAP TM HTN) quality improvement program. Methods: To estimate the 10-year health and economic outcomes achieved when healthcare delivery systems adopt MAP HTN, HealthPartners Institute's ModelHealth™: Cardiovascular disease (CVD) microsimulation model was applied to conduct “what if” analyses for adopting MAP HTN nationwide and in care delivery systems of varying size. MAP HTN targets patients aged 18 to 85 years with diagnosed hypertension. To account for newly diagnosed hypertensive patients over time and outcomes that follow exposure to the program after age 85, the simulation model predicted outcomes for a dynamic cross-section of U.S. adults aged 18 to 100 years. This study was conducted from the health care sector perspective, including that of the payers for medical services and the health care delivery system that provides these services. Risk equations to predict the incidence of CVD events were derived from Framingham Heart Study data and are based upon the following risk factors: age, sex, body mass index, systolic BP (SBP), lipid levels, smoking status, diabetes status, and CVD history. Modeled outcomes over 10 years included prevalence of SBP ≥140 mmHg; incidence of cardiovascular disease (CVD) events and CVD-related mortality; averted medical costs in 2021 U.S. dollars; and net costs. Results: Nationwide adoption of MAP HTN was predicted to improve mean BP control by 4.3% and prevent 601000 CVD events, with 85000 fewer myocardial infarctions, 164000 fewer strokes and 120000 fewer CVD deaths over 10 years as compared to usual care. The majority (93%) of the estimated $17.9 billion in averted costs were among Medicare beneficiaries. Total costs of implementing MAP HTN was $12.6 billion, resulting in a predicted $5.3 billion in net savings to the health care sector over 10 years. Small and large care delivery organizations could economically benefit from implementing MAP HTN at varying levels (5-50%) of shared cost savings from payers. Conclusions: The model demonstrates that AMA’s MAP HTN has the potential to improve population health and provide a positive business case by averting future myocardial infarctions, strokes, and CVD deaths.

How is diagnostic uncertainty communicated and managed in real world primary care settings?

BackgroundManaging diagnostic uncertainty is a major challenge in primary care due to factors such as the absence of definitive tests, variable symptom presentations and disease evolution. Maintaining patient trust during a period of investigative uncertainty, whilst minimising scope for diagnostic error is a challenge. Mismanagement can lead to diagnostic errors, treatment delays, and suboptimal patient outcomes.ObjectiveOur aim was to explore how UK primary care physicians (GPs) address and communicate diagnostic uncertainty in practice.DesignThis qualitative study used video and audio-recordings. Verbatim transcripts were coded with a modified, validated tool to capture GPs’ actions and communication in primary care consultations that included diagnostic uncertainty. The tool includes items relating to advice regarding new symptoms or symptom deterioration (sometimes called ‘safety netting’). Video data was analysed to identify GP and patient body postures during and after the delivery of the management plan.ParticipantsAll patient participants had a consultation with a GP, were over the age of 50 and had (1) at least one new presenting problem or (2) one persistent problem that was undiagnosed.ApproachData collection occurred in GP-patient consultations during 2017–2018 across 7 practices in UK during 2017–2018.Key resultsGPs used various management strategies to address diagnostic uncertainty, including (1) symptom monitoring without treatment, (2) prescribed treatment with symptom monitoring, and (3) addressing risks that could arise from administrative tasks. GPs did not make management plans for potential treatment side effects. Specificity of uncertainty management plans varied among GPs, with only some offering detailed actions and timescales. The transfer of responsibility for the management plan to patients was usually delivered rather than negotiated, with most patients confirming acceptance before concluding the discussion.ConclusionsWe offer guidance to healthcare professionals, improving awareness of using and communicating management plans for diagnostic uncertainty.

Effectiveness of pharmacist intervention on antidepressant medication knowledge, necessity, and concerns beliefs in patients with depression

Abstract Background Sub-optimal patient medication knowledge, negative beliefs, and concerns about the potential of medications to cause harm are important contributory factors to non-adherence in patients with depression. Objectives To assess the effectiveness of a pharmacist intervention on antidepressant medication knowledge, necessity, and concerns beliefs in patients with depression. Methods A prospective interventional study (approval number: ABUCUHSR/2020/018) was carried out between April 2019 and March 2020 at the Federal Neuro-Psychiatric Hospital, Maiduguri, Nigeria. Patients were randomized into the usual care or intervention group. The intervention consisted of an educational programme, and data were collected at baseline, 3 months, and 6 months. Patient knowledge about medicines and beliefs about medicines questionnaires were used to assess patient knowledge and beliefs. Key findings A total of 49 (81.6%) and 52 (86.6%) participants completed the 6-month follow-up in the usual care and intervention groups, respectively. Significant improvements in mean medication knowledge scores of participants in the intervention group were observed at 3 months (P < .001) and 6 months (P < .001), respectively. Significant improvements in mean medication necessity and concerns belief scores of participants in the intervention group were also observed at 3 months (P < .001) and 6 months (P < .001), respectively. Medication knowledge (P < .001, partial η2 = 0.203), medication necessity (P < .001, partial η2 = 0.329), and medication concerns (P < .001, partial η2 = 0.163) positively changed with time. Conclusion Pharmacist intervention significantly improved antidepressant medication knowledge, necessity, and concerns beliefs in patients with major depressive disorder.